A slot-based energy storage decision-making approach for optimal Off-Grid telecommunication operator

TL;DR

This paper introduces a slot-based Markov Decision Process model for off-grid telecommunication energy management, optimizing battery use and energy sales amid variable conditions and operational constraints.

Contribution

It develops a novel slot-based MDP framework tailored for off-grid telecom energy storage, incorporating real-world factors like non-stationary energy and demand, with proven computational efficiency.

Findings

Optimal policies effectively balance energy sales and storage.

Model adapts to city-specific and seasonal energy patterns.

Results demonstrate improved reward maximization across different scenarios.

Abstract

This paper proposes a slot-based energy storage approach for decision-making in the context of an Off-Grid telecommunication operator. We consider network systems powered by solar panels, where harvest energy is stored in a battery that can also be sold when fully charged. To reflect real-world conditions, we account for non-stationary energy arrivals and service demands that depend on the time of day, as well as the failure states of PV panel. The network operator we model faces two conflicting objectives: maintaining the operation of its infrastructure and selling (or supplying to other networks) surplus energy from fully charged batteries. To address these challenges, we developed a slot-based Markov Decision Process (MDP) model that incorporates positive rewards for energy sales, as well as penalties for energy loss and battery depletion. This slot-based MDP follows a specific structure we have previously proven to be efficient in terms of computational performance and accuracy. From this model, we derive the optimal policy that balances these conflicting objectives and maximizes the average reward function. Additionally, we present results comparing different cities and months, which the operator can consider when deploying its infrastructure to maximize rewards based on location-specific energy availability and seasonal variations.